Providing the desired performance and adaptive robust control for a dynamic process having delays and nonlinearities are a current challenge in the control field. Significant progress has been made relative to such control but there is still substantial room for improvement. An increase in computational power has given a significant incentive to control advancements. The use of neural networks for identification and control of such processes is presently being investigated by researchers.
Neural networks have been used successfully is many control applications, however, the use of such networks in controlling a dynamic process is still in its infancy. A feedback procedure commonly known as "backpropagation" and "delta rule" have been used in some methods. For self-tuning and adaptive control, backpropagation is utilized for nonlinear dynamic processes where the dynamics of the process are assumed to have feedback linearizable form, and control values are calculated directly from the neural network estimated model. Several learning and control methods have been proposed for neural network control. Typically, the most common methods utilize off-line training for non-dynamic processes. Another approach involves estimating the inverse dynamics of processes by minimizing the error between the process input and the inverse dynamic output. This inverse model, in turn, is used as a feedforward type controller to provide a minimum error between the desired and actual value of the process.
All of the foregoing approaches have certain inherent disadvantages. For example, the method and procedure of a particular approach may be restricted to a certain type of process with a defined, rather than a general, structure. In addition, the approach might require that the process have an equal number of input and output variables for implementation purposes. Alternatively, training a neural network feedforward control based on minimizing the error between the control value entering the process and the trained control value produced by the inverse process does not necessarily minimize the error between the desired and the actual process values. Furthermore, using a combination of any of the foregoing approaches cannot eliminate problems since specialized learning typically is not readily adaptable to an on-line application for a dynamic process.
Because of the foregoing, it has become desirable to develop a method and procedure for a neural network control for dynamic processes which considers both the error between the desired process value and the actual process output and the error between the desired process value and the inverse process value (trained control value) in order to eliminate the problems associated with the learning activity.